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Record W2791548769 · doi:10.1017/s1463423618000099

Moving beyond depression screening: integrating perinatal depression treatment into OB/GYN practices

2018· article· en· W2791548769 on OpenAlexfundno aff
Christina Terrazas, Lisa S. Segre, Cheryl Wolfe

Bibliographic record

VenuePrimary Health Care Research & Development · 2018
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsnot available
FundersNational Institute of Mental HealthCanadian Wildlife Health CooperativeIowa Department of Public Health
KeywordsRandomized controlled trialDepression (economics)MedicineAnxietyFamily medicineClinical PracticePsychiatryActive listeningPsychologyPsychotherapist

Abstract

fetched live from OpenAlex

In 2015, the American College of Obstetricians and Gynecologists issued a recommendation to screen women for depression and anxiety symptoms at least once during the perinatal period. Nevertheless, many identified women will not receive care from a behavioral health specialist. Listening Visits (LV), developed for delivery by nurses and validated in the United Kingdom, have recently been evaluated in a US-based randomized controlled trial (RCT) which recruited research participants from three home-visiting programs and an urban OB/GYN practice. RCT results indicated clinically and significant improvement in depression symptoms. To bridge the gap between evidence and practice, and based on experiences garnered at the OB/GYN site during the RCT, this development paper proposes a strategy for implementing depression screening and LV into routine clinical care in this practice setting.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.062
GPT teacher head0.435
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2018
Admission routes1
Has abstractyes

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